No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by anshaneja5 · Agent Tool · ★ 66
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is markscrub safe to install? View the security audit →
markscrub CLI + universal agent skill to scrub AI provenance marks from text and files — for privacy and hygiene on content you own. Honesty: Layer A and metadata cleans are verifiable. Layer B is best-effort. No tool can certify that a vendor detector will fail. Want a website instead of a CLI? Use Claude Watermark Remover — same project, hosted. Free in-browser check/clean (Layer A). Paid rewrite for the statistical text mark (Layer B). No install. Use this repo if you want local files, C2PA/metadata, CI, or an agent skill. Try Layer A locally in-browser: open Install Dev without build: Agent skill (Cursor / Claude Code / Codex) Or copy/symlink [skills/remove-ai-mar
| Stars | 66 |
| Forks | 6 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 64.1481463970617/100 |
| Last Updated | 2026-09-02 |
| Created | 2026-08-12 |
| Platforms | cli, node |
| Est. Tokens | ~4k |
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markscrub is CLI + agent skill to scrub AI provenance marks from text and files. It is categorized as a Agent Tool with 66 GitHub stars.
markscrub is primarily written in TypeScript.
You can find installation instructions and usage details in the markscrub GitHub repository at github.com/anshaneja5/markscrub. The project has 66 stars and 6 forks, indicating an active community.
markscrub is released under the MIT license, making it free to use and modify according to the license terms.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: